Pathway management using model analysis and forecasting

ABSTRACT

A computer generates a three dimensional map of a pathway area using a plurality of overhead images. The computer determines a forecasted weather pattern to occur in the pathway area. The computer analyzes the three dimensional map and the forecasted weather pattern to predict one or more violations of the pathway. The computer generates a priority for the one or more predicted violations of the pathway. The computer generates a plan for pathway management of the pathway area.

FIELD OF THE INVENTION

The present invention relates generally to the field of pathwaymanagement, and more particularly to vegetation management in or near apathway corridor.

BACKGROUND OF THE INVENTION

For many utilities, vegetation is the number-one cause of all unplanneddistribution outages. Most damage to electric utility systems duringstorms is caused by a falling tree or branch that takes power lines outof service. In order to help reduce the frequency of tree damage toutility systems, many utilities implement vegetation management programsas a preventative measure.

Traditionally, vegetation management programs have relied on regularsurveying and pruning by arborist teams to help control vegetationaround utility systems, but the sheer number of utility lines coveringvast distances makes it impractical, in many cases, to send survey teamson the ground. Even so, it is often necessary for field personnel tovisit the site in person in order to decide the type of maintenanceneeded to resolve the encroachment of the vegetation.

In an effort to expedite the process of vegetation management, manyutility companies utilize aerial reconnaissance techniques to providephotographic imagery of their utility systems which can be examined forpossible vegetation growth issues. Typically, aerial photography, lightdetection and ranging (LIDAR), synthetic aperture radar, thermal imagingand other types of remote sensing technologies capture digital imageryof real-world scenes for the purpose of extracting three-dimensionalpoint coordinate (spatial geometry) data. These technologies, thoughcost prohibitive, are widely used in industry to collect the datanecessary for map-making, and capture spatial (point coordinate) data ina digital form that allows a wide variety of computer-based tools to beapplied to the tasks of map-making, 3D modeling for engineeringanalysis, vegetation assessment/management, and/or asset management.

SUMMARY

Embodiments of the present invention disclose a method, computer programproduct, and system for pathway management. A computer generates a threedimensional map of a pathway area using a plurality of overhead images.The computer determines a forecasted weather pattern to occur in thepathway area. The computer analyzes the three dimensional map and theforecasted weather pattern to predict one or more violations of thepathway. The computer generates a priority for the one or more predictedviolations of the pathway. The computer generates a plan for pathwaymanagement of the pathway area.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a functional block diagram illustrating a pathway managementenvironment, in accordance with an embodiment of the present invention.

FIG. 2 illustrates operational steps of a ROW analyzer program, on acomputing device within the data processing environment of FIG. 1, inaccordance with an embodiment of the present invention.

FIG. 3 depicts a block diagram of components of the computing deviceexecuting the ROW analyzer program, in accordance with an embodiment ofthe present invention.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer-readablemedium(s) having computer readable program code/instructions embodiedthereon.

Any combination of computer-readable media may be utilized.Computer-readable media may be a computer-readable signal medium or acomputer-readable storage medium. A computer-readable storage medium maybe, for example, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of a computer-readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on a user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer, other programmabledata processing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce acomputer-implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Three-dimensional (3D) coordinate point data and 3D imagery of areal-world scenes are often used in the field when vegetation managementis performed. The location of individual points in 3D space can becompared to the locations of recognizable objects within the real-worldscene and a determination made as to whether or not specific clearancedistances are maintained between the recognizable objects (e.g., a powerline) and a potential violating object (e.g., a tree).

The task of discerning the existence of clearance violations (a.k.a.,interferences) between a recognizable object and a potential violatingobject, within the real-world scene of multiple available objects, isoften carried out using computer analysis. Few methods/approaches lendthemselves to being implemented “in the field” or out in the physicalreal world where the physical objects actually exist. The dataprocessing events, computing power/equipment requirements, and datavolumes are often simply too large to easily handle in the field usingcurrent approaches. Meanwhile, manual methods require gross estimates ofchanging physical conditions and accuracy is limited, so solutions tovegetation management such as clear-cutting currently prevail.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram illustrating a pathwaymanagement environment, generally designated 100, in accordance with oneembodiment of the present invention.

In this exemplary embodiment, a pathway can include a trail, astreet/roadway, a sidewalk, a track, a path, a railway, a body of water(such as a stream, a river, or a lake), a right of way, or anotherequivalent structure known to those skilled in the art. To keep thedescription simple, the pathway in this exemplary embodiment will oftenbe referred to as a right of way (ROW), which is not to be interpretedas a limitation.

Pathway management environment 100 includes computing device 110,satellite 160, storage device 140 and mobile device 150, allinterconnected over network 130. Computing device 110 includes ROWanalyzer program 125, map generator 120, weather forecaster 122, andstorage device 170, which further includes priority map 135, weatherdata 114, utility data 116, and image data 118.

In alternative embodiments, ROW analyzer program 125, map generator 120,weather forecaster 122, priority map 135, weather data 114, utility data116, and image data 118 may be stored externally to computing device 110and accessed through network 130. Network 130 can be, for example, alocal area network (LAN), a wide area network (WAN) such as theInternet, or a combination of the two, and may include wired, wireless,fiber optic or any other connection known in the art. In general,network 130 can be any combination of connections and protocols thatwill support communications between computing device 110, storage device140, and mobile device 150 in accordance with a exemplary embodiment ofthe present invention.

In various embodiments of the present invention, computing device 110,satellite 160, storage device 140, and mobile device 150 can be servers,laptop computers, tablet computers, netbook computers, personalcomputers (PCs), desktop computers, personal digital assistants (PDAs),smart phones, satellites or any programmable electronic device capableof communication via network 130. In other exemplary embodiments,computing device 110 represents a computing system utilizing clusteredcomputers and components to act as a single pool of seamless resources.In general, computing device 110 can be any computing device or acombination of devices with access to ROW analyzer program 125, mapgenerator 120, weather forecaster 122, storage device 170, priority map135, weather data 114, utility data 116, and image data 118, and iscapable of executing ROW analyzer program 125, weather forecaster 122,and map generator 120. Computing device 110, satellite 160, and mobiledevice 150 may include internal and external hardware components, asdepicted and described in further detail with respect to FIG. 3.

In an exemplary embodiment, satellite 160 is a satellite in orbit aroundthe planet Earth. Satellite 160 is capable of taking images of the Earthwith sufficient resolution to distinguish vegetation. The images takenof the Earth are sent to map generator 120 as needed or requested. Insome embodiments, the images are sent to a database, such as storagedevice 140, for storage and access by map generator 120.

In an exemplary embodiment, storage device 140 is a storage device thatstores information such as satellite images, records of weather, andutility information such as maps with the location oftransmission/distribution assets. Typically, this information isaccessed as needed by computing device 110 via network 130. In someembodiments, storage device 140 is integral with computing device 110.

In an exemplary embodiment, mobile device 150 is a portable computingdevice such as a laptop, tablet, smartphone, or PDA. In general, mobiledevice 150 is operated by a user located in the field (i.e. in or nearthe ROW corridor). Mobile device 150 can send and receive data such asROW maps, work schedules, and progress updates via network 130.

In some embodiments, the functions of ROW analyzer program 125, mapgenerator 120, weather forecaster 122, storage device 170, priority map135, weather data 114, utility data 116 and image data 118 can beincluded in a single program or in a combination of programs operatingon computing device 110. In general, the individual functions of ROWanalyzer program 125, map generator 120, weather forecaster 122, storagedevice 170, priority map 135, weather data 114, utility data 116 andimage data 118 require computational and data storage requirements thatcan exceed the capabilities of individual computing devices. Therefore,in an exemplary embodiment computing device 110 can be best representedas a cloud computing system capable of meeting the computational anddata storage requirements.

In an exemplary embodiment, on computing device 110, ROW analyzerprogram 125 generates ROW (pathway) management maps and work schedules.The generated ROW maps and work schedules are saved as priority map 135in storage device 170. Storage device 170 is, in general, a computerreadable memory, included in computing device 110 as part of persistentstorage 408 (FIG. 4), with the capacity to facilitate the large amountof data stored as priority map 135, weather data 114, utility data 116and image data 118. In other embodiments, storage device 170 is acombination of separate storage devices (located internally orexternally of computing device 110) that are accessible by computingdevice 110. In yet other embodiments, storage device 170 is included instorage device 140 and is accessible by computing device 110 via network130.

In an exemplary embodiment, weather forecaster 122 stores and retrievesdata located in weather data 114 and utility data 116. In general,weather forecaster 122 uses the data stored in weather data 114 andutility data 116 to generate forecasted weather patterns for a givenpathway (ROW) corridor. The aforementioned forecasted weather patternstypically include predicted average weather patterns as well aspredicted severe weather patterns, such as weather patterns that exceeda threshold value. For example, a typical forecasted weather patternincludes averages of precipitation, wind speed, and temperature, as wellas records of precipitations, wind speeds, and temperatures thatdeviated from those respective averages by twenty percent or more (i.e.,severe weather patterns). In some embodiments, a series of forecastedweather patterns may be produced using various ranges of weather data(e.g., weather data covering five, ten and twenty year periods).

In an exemplary embodiment, weather data 114 is stored in storage device170 and includes the weather history for an area that includes at leastpart of a ROW corridor. Typically, the weather history spans at leastseveral years. The weather history of multiple regions or areas may needto be combined to cover an entire ROW corridor. If new weather historyis required by weather forecaster 122, then weather forecaster 122 willutilize a search function to locate, access and save a copy of the newweather history as weather data 114.

In an exemplary embodiment, utility data 116 is stored in storage device170 and includes maps showing the locations of transmission/distributionassets, which include structures such as electrical power lines andcommunication towers. Utility data 116 can also include geological data,topographical data, and waterway data for a ROW corridor and thesurrounding areas. If weather forecaster 122 and/or map generator 120require new utility data, then weather forecaster 122 and/or mapgenerator 120 utilizes a search function to locate, access and save acopy of the new utility data as utility data 116.

In an exemplary embodiment, map generator 120 stores and retrieves datalocated in utility data 116 and image data 118. In general, mapgenerator 120 generates 3D maps of a given ROW corridor. The generated3D maps can include vegetation locations, vegetation densities,vegetation height, topographical data, geographical data, and waterwaydata for a given ROW corridor. To generate the 3D map at least twosatellite images, taken from two different viewing angles, are overlaid.A computer analysis of the overlaid images generates a 3D (Recovered 3DDisparity Map) picture showing the location and densities of vegetationin or near the ROW corridor. In general, a Recovered 3D Disparity Map isgenerated through the computer analysis of at least two rectified imagestaken from different vantage points. The computer analysis yields pointcorrespondences and computation of the apparent shift (disparity) of thepoint gives information about relative depth of the scene. In otherwords, by comparing the apparent change in point location (of anobject), in two or more images, the apparent size, shape, andorientation of an object can be estimated. There are a number ofavailable options (algorithms) which are well known in the art that arecapable of performing these calculations. As such, a deeper explanationof generating recovered 3D disparity maps would exceed the scope of thisdiscussion. To further enhance the accuracy of the 3D map knowngeological, topographical, and waterway data can be added to yield ahighly accurate 3D map of the ROW corridor and surrounding area. In someembodiments, a variety of filters and satellite image resolutions can becombined to identify sick or dead vegetation, such as dead trees.

In an exemplary embodiment, map generator 120 uses the images stored inimage data 118 for the generation of three dimensional (3D) maps of agiven ROW corridor. If new overhead images are required by map generator120 then map generator 120 will utilize a search function to locate,access and save a copy of the new overhead images as image data 118. Insome embodiments, map generator 120 also includes a request program thatallows map generator 120 to send commands (to, for example, satellite160) to take new overhead images of a ROW corridor and/or nearby areas.

In an exemplary embodiment, image data 118 is stored in storage device170 and includes overhead images taken from a number of aerial and/orouter-space vantage points. Typically, the overhead images includemultiple satellite images of a given ROW corridor and can include imagestaken from aerial platforms airplanes, drones. In some embodiments, theimages stored in image data 118 can include non-aerial/non-outer-spaceimages, for example, survey images taken at ground level. In general,the images stored in image data 118 can be taken using a variety ofspectroscopic regions, spectroscopic filters, and can include imagesthat have been modified by a computer (i.e., a computer enhanced image).

In an exemplary embodiment, ROW analyzer program 125 combines the ROWmaps, generated by map generator 120, with the weather predictions fromweather forecaster 122 to generate ROW maps and schedules for ROWcorridor management. In general, ROW analyzer program 125 utilizesmachine-learning driven forecasting (described in further detail below)to predict which “high risk” vegetation or areas of “high risk”vegetation that will most likely produce violations of the pathway(i.e., violating objects). Once the potential violations of the pathwayhave been identified, ROW analyzer program 125 produces maps indicatingtheir location along with their respective schedules for management,which are saved as priority map 135 that is stored in storage device170. Priority map 135 includes, in general, a plan generated for pathwaymanagement. The plan typically includes a map, a prioritized list, aschedule, a location of a predicted violation of the pathway, and/or alocation of a current violation of the pathway.

The machine-learning driven forecasting, used by ROW analyzer program125, breaks the complex process of prediction/forecasting intomanageable steps. In general, ROW analyzer program 125 usesensemble-based modeling to simplify the process of forecasting andprediction. An ensemble is a large collection of weak, localizedpredictors that generate short-term local-level predictions ofviolations of the pathway (ROW risks). The ensembles may take intoaccount the local terrain/geographic features, weather patterns, andvegetation (such as tree density) to make predictions. Each localpredictor can be composed of simple predictor functions to take datainputs and render a prediction based on the input data. In general, thecharacteristics (e.g., weather history, geographic features, history ofproducing violating objects) associated with a given point are analyzedusing non-parametric analysis (a type of statistical analysis). Thenon-parametric analysis is a simple predictor function, and theassociated result is the prediction. There are a large number ofpossible algorithms, which are well known in the art, that can be usedto perform the non-parametric analysis and a deeper discussion of thisprocess exceeds the scope of the present disclosure. In summation, theensemble, relies on a very large number of localized predictions.

However, the predictions/forecasts produced by ROW analyzer program 125are not limited to the initial short-term local-level predictions ofvarious ROW risks. ROW analyzer program 125 incorporates neighboreffects (described in further detail below) into the ensemble to createone or more large scale, long-term, regional-level or global predictionmodels, or any combination thereof. ROW analyzer program 125 enhancespredictions, produced by the ensemble, by taking into account and/orpredicting more long-range phenomenon, which yields strongerpredictions. In general, each localized weak predictor, whiletransforming inputs into a prediction, takes into account theprediction(s) of its surrounding neighbor(s) (i.e. a cumulative neighboreffect) to make a more accurate prediction of violations of the pathway.In other words, if the neighbors of a particular weak predictor arepredicting high risk then it is likely, though not necessary, that theweak predictor will yield a final high-risk prediction due to acumulative effect from the high risk neighbors.

For example, an analysis of a dead tree yields a high probability thatit may fall over in the near future. The tree is far enough away from apower line that if the tree fell over the tree would not generate aviolating object. However, the tree is on a hill and when the effect ofa potential, and highly probable, summer wind is taken into account afinal prediction is that the tree will likely impact the power line orcause other vegetation to impact the power line, i.e., the creation of aviolation of the ROW is predicted. In another example, a dead tree islocated one hundred feet from the bank of a river subject to annualflooding and five hundred feet from an overflow intake of a powergenerating dam. A weak, localized predictor indicates that the dead treeposes little threat and will not generate a violating object. However,when the effects of an annual flood are taken into account the dead treegenerates a prediction indicating a high probability of blockageformation in the overflow intake, which would pose considerable hazardto the structural integrity of the dam.

FIG. 2 is a flowchart depicting the operational steps utilized by ROWanalyzer program 125 for the generation of ROW maps and schedules forROW corridor management, in accordance with an embodiment of the presentinvention.

In this embodiment, ROW analyzer program 125 identifies a section(s) ofa ROW corridor that requires vegetation management. In general, a listof section(s) of a ROW corridor are analyzed on a scheduled basis.However, there are sections which would be more likely to requirevegetation management due to water supply and average sunlight, i.e., asunny, well watered area will be more likely to experience rapidvegetation growth. Therefore, these sections would be entered inmultiple places in the list. There is also a manual entry option where auser can select a section of interest for processing. This could be ofgreat use when, for example, a storm has been predicted. ROW analyzerprogram 125 then sends data identifying the section(s) of ROW corridor,which will be processed, to map generator 120 and to weather forecaster122 (step 210).

Map generator 120 and weather forecaster 122 search weather data 114,utility data 116 and image data 118 for the required information togenerate maps and weather forecasts respective to the ROW corridor andthe identified section(s) of the ROW corridor (step 220). If therequired information is found (decision 225, yes branch), then ROWanalyzer program 125 generates maps and weather forecasts (steps 235 and240 respectively). If the required information is not found (decision225, no branch), then map generator 120 and/or weather forecaster 122retrieves the required data from alternate sources such as storagedevice 140 and/or instructs satellite 160 to take/send the requiredimages (step 230).

Once the required information has been retrieved and respectively savedto storage device 170 as weather data 114, utility data 116 and imagedata 118 then ROW analyzer program 125 executes map generation (step235) and weather forecast prediction (step 240). ROW analyzer program125 uses map generator 120 to analyze a series of satellite images andgenerate 3D maps of a ROW corridor and the identified a section(s) ofthe ROW corridor (step 235). The images are overlaid and analyzed togenerate data values for the vegetation height, density, and distancefrom transmission/distribution assets. In a further spectral analysis ofthe images, map generator 120 identifies the type/size of vegetation(e.g., bushes, trees) as well as sick and/or dead vegetation. Forexample, spectral analysis shows the presence of tree trunks but notleaves. ROW analyzer program 125 then labels the trees with no leaves asdead and as having a higher probability of generating a violatingobject.

In an alternate embodiment, ROW analyzer program 125 can leveragepreviously created maps and weather patterns to determine if a newpriority map should be generated. For example, a first section of an ROWcorridor has produced numerous violating objects over a one year timespan. The number and nature of violating objects produced exceeds anestimated (statistical) production rate for the first section.Therefore, ROW analyzer program 125 proceeds with the generation of anew priority map. In another example, a second section of an ROWcorridor has produced one violating object over a one year time span.The number and nature of violating object produced falls well below anestimated (statistical) production rate for the first section.Therefore, ROW analyzer program 125 postpones the generation of a newpriority map for the second section for another year.

In step 240 of this embodiment, ROW analyzer program 125 uses weatherforecaster 122 to analyze weather data and create average weatherpatterns for an area including the ROW corridor. Weather forecaster 122also predicts the extreme weather conditions most likely to occurnear/in the ROW corridor and the identified a section(s) of the ROWcorridor. For example, using the weather averages during the periods ofthe last five, ten, twenty five and fifty years a series of averageweather patterns are produced and used to extrapolate the most probableaverage weather and the most probable extreme weather conditions for aROW corridor and the surrounding area.

In this embodiment, ROW analyzer program 125 generates detailedtopographical maps of the ROW corridor (step 250). First, crudetopographical maps of the ROW corridor are obtained from utility data116 and/or storage device 140. The crude topographical map typically is(or can be based on) publically available topographical maps which arethen combined with the generated 3D maps of a ROW corridor. Byoverlaying crude topographical maps over the generated 3D maps a moredetailed hybrid map (3D vegetation, and topographical) is produced. Themap shows in detail the position of and height of vegetation respectiveto the ground and the transmission/distribution assets. The level ofdetail of the hybrid map can be further enhanced by overlaying images(from various vantage points and resolutions) taken in a variety ofspectral regions. For example, images taken in the ultra violet andmicrowave regions are overlaid to enhance the detail resolution in areasof the map that are covered by water and dense vegetation. In anotherexample, a green light filter is applied to enhance the resolution ofthe satellite images taken in the visible light region.

ROW analyzer program 125 analyzes data (such as known mechanical limitsof materials) and maps (such as the topographical and ROW corridor maps)with respect to the predicted average weather patterns and the extremeweather conditions likely to occur near/in the ROW corridor (step 260).The analysis allows effects such as changes in wind speed due to thepresence of hills and valleys to be predicted. These effects are thenused to predict specific conditions that are most likely to generatepotential violating objects and/or predicted violations of the pathway.ROW analyzer program 125 analyzes the hybrid map using the specificconditions that are most likely to generate potential violating objectsand/or predicted violations of the pathway and assigns a priority valueto them. There are two general categories of potential violating objectsand/or predicted violations of the pathway that are identified. Thefirst category includes non-weather related potential violating objectsand/or predicted violations of the pathway (i.e., vegetation growthand/or dead vegetation). The second category includes weather relatedpotential violating objects and/or predicted violations of the pathway(i.e., a tree growing on the top of a hill that will likely be uprootedduring a storm and land on an electric power line).

ROW analyzer program 125 uses the prediction(s) (of step 260) togenerate a priority map showing the location of vegetation (and otherobjects and/or conditions) that are or will likely generate potentialviolating objects and/or predicted violations of the pathway (step 270).A copy of the priority map and its associated information (i.e.,potential violating objects and/or predicted violations of the pathway)is saved in priority map 135. The priority map and the associatedinformation are then passed to mobile device 150, which is in the field.The operator uses the priority map information to manage the potentialviolating objects in a given section of ROW corridor. For example, atree located high on the side of a hill has been identified as apotential violating object with a high priority value. The tree isremoved first and then, as time permits, other potential violatingobjects with lower priority value are removed.

FIG. 3 depicts a block diagram of components of computing device 110,satellite 160, and mobile device 150 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.3 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Computing device 110, satellite 160, storage device 140, and mobiledevice 150 respectively include communications fabric 402, whichprovides communications between computer processor(s) 404, memory 406,persistent storage 408, communications unit 410, and input/output (I/O)interface(s) 412. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer-readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM) 414 and cache memory 416. In general, memory 406 can include anysuitable volatile or non-volatile computer-readable storage media.

ROW analyzer program 125, map generator 120, weather forecaster 122,priority map 135, weather data 114, utility data 116, and image data 118are stored in persistent storage 408, which includes storage device 170,for execution and/or access by one or more of the respective computerprocessors 404 via one or more memories of memory 406. In thisembodiment, persistent storage 408 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 408 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer-readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive may be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage408.

Communications unit 410, in these examples, provides for communicationswith other data processing systems or devices, including resources ofsatellite 160, storage device 140, and mobile device 150. In theseexamples, communications unit 410 includes one or more network interfacecards. Communications unit 410 may provide communications through theuse of either or both physical and wireless communications links. ROWanalyzer program 125, map generator 120, weather forecaster 122,priority map 135, weather data 114, utility data 116, and image data 118may be downloaded to persistent storage 408 through communications unit410.

I/O interface(s) 412 allows for input and output of data with otherdevices that may be connected to computing device 110. For example, I/Ointerface 412 may provide a connection to external devices 418 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 418 can also include portable computer-readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, e.g., ROW analyzer program 125,map generator 120, weather forecaster 122, priority map 135, weatherdata 114, utility data 116 and image data 118, can be stored on suchportable computer-readable storage media and can be loaded ontopersistent storage 408 via I/O interface(s) 412. I/O interface(s) 412also connect to a display 420.

Display 420 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for pathway management, the methodcomprising: a computer generating a three dimensional map of a pathwayarea using a plurality of overhead images; the computer determining aforecasted weather pattern to occur in the pathway, based on, at leastin part, previous weather patterns for the pathway area; the computeranalyzing the three dimensional map and the forecasted weather patternto determine an object, a state of the object, and an event that arelikely to cause a violation of the pathway area if the forecastedweather pattern occurs; the computer generating a priority for one ormore likely violations of the pathway; and the computer generating aplan for pathway management of the pathway area based, at least in part,on the priority for the one or more likely violations of the pathway,wherein the plan guides maintenance activity that reduces a likelihoodof occurrence of the violation of the pathway by the object prior to anoccurrence of the forecasted weather pattern.
 2. The method of claim 1,wherein the plurality of overhead images indicate one or both of alocation of the object and the state of the object, and wherein theplurality of overhead images include one or more of overhead imagestaken from a plurality of vantage points, overhead images taken using avariety of spectroscopic regions, overhead images taken using a varietyof spectroscopic filters, and overhead images that have been modified bya computer.
 3. The method of claim 1, wherein the forecasted weatherpattern includes one or more of an average of precipitation, an averagewind speed, an average temperature, and records of precipitation, windspeed, and temperature which deviate from the respective average by aspecified threshold.
 4. The method of claim 1, wherein the plan forpathway management includes one or more of a map, a prioritized list, aschedule, and a location of a predicted violation of the pathway.
 5. Themethod of claim 1, wherein the step of the computer analyzing the threedimensional map and forecasted weather pattern to determine an object, astate of the object, and an event that are likely to cause a violationof the pathway area if the forecasted weather pattern occurs includes:the computer generating long-term regional-level predictions byanalyzing a plurality of weak predictors that generate short-termlocal-level predictions, wherein the long-term regional-levelpredictions indicate the object, the state of the object, and the eventthat are likely to cause the violation of the pathway area by theobject.
 6. The method of claim 5, wherein the analysis of the pluralityof weak predictors that generates short-term local-level predictionsincludes an analysis of one or more of a local geographic feature, theforecasted weather pattern, and a vegetation.
 7. The method of claim 5,wherein the generation of the long-term regional-level predictionsincludes analyzing a cumulative effect of the plurality of short-termlocal-level predictions.
 8. A computer program product for pathwaymanagement, the computer program product comprising: one or morecomputer-readable storage media and program instructions stored on theone or more computer-readable storage media, the program instructionscomprising: program instructions to generate a three dimensional map ofa pathway area using a plurality of overhead images; programinstructions to determine a forecasted weather pattern to occur in thepathway area, based on, at least in part, previous weather patterns forthe pathway area; program instructions to analyze the three dimensionalmap and the forecasted weather pattern to determine an object, a stateof the object, and an event that are likely to cause a violation of thepathway area by the object if the forecasted weather pattern occurs;program instructions to generate a priority for one or more likelyviolations of the pathway; and program instructions to generate a planfor pathway management of the pathway area based, at least in part, onthe priority for the one or more likely violations of the pathway,wherein the plan guides maintenance activity that reduces a likelihoodof occurrence of the violation of the pathway by the object prior to anoccurrence of the forecasted weather pattern.
 9. The computer programproduct of claim 8, wherein the plurality of overhead images indicateone or both of a location of the object and the state of the object, andwherein the plurality of overhead images include one or more of overheadimages taken from a plurality of vantage points, overhead images takenusing a variety of spectroscopic regions, overhead images taken using avariety of spectroscopic filters, and overhead images that have beenmodified by a computer.
 10. The computer program product of claim 8,wherein the forecasted weather pattern includes one or more of anaverage of precipitation, an average wind speed, an average temperature,and records of precipitation, wind speed, and temperature which deviatefrom the respective average by a specified threshold.
 11. The computerprogram product of claim 8, wherein the plan for pathway managementincludes one or more of a map, a prioritized list, a schedule, alocation of a predicted violation of the pathway, and a location of apredicted violation of the pathway.
 12. The computer program product ofclaim 8, wherein the program instructions to analyze the threedimensional map and forecasted weather pattern to determine an object, astate of the object, and an event that are likely to cause a violationof the pathway area if the forecasted weather pattern occurs includes:program instructions to generate long-term regional-level predictions byanalyzing a plurality of weak predictors that generate short-termlocal-level predictions, wherein the long-term regional-levelpredictions indicate the object, the state of the object, and the eventthat are likely to cause the violation of the pathway area by theobject.
 13. The computer program product of claim 12, wherein theanalysis of the plurality of weak predictors that generates short-termlocal-level predictions includes an analysis of one or more of a localgeographic feature, the forecasted weather pattern, and a vegetation.14. The computer program product of claim 12, wherein the generation ofthe long-term regional-level predictions includes analyzing a cumulativeeffect of the plurality of short-term local-level predictions.
 15. Acomputer system for pathway management, the computer system comprising:one or more processors, one or more computer-readable memories, one ormore computer-readable tangible storage devices, and programinstructions stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, the program instructions comprising:program instructions to generate a three dimensional map of a pathwayarea using a plurality of overhead images; program instructions todetermine a forecasted weather pattern to occur in the pathway area,based on, at least in part, previous weather patterns for the pathwayarea; program instructions to analyze the three dimensional map and theforecasted weather pattern to determine an object, a state of theobject, and an event that are likely to cause a violation of the pathwayarea by the object if the forecasted weather pattern occurs; programinstructions to generate a priority for one or more likely violations ofthe pathway; and program instructions to generate a plan for pathwaymanagement of the pathway area based, at least in part, on the priorityfor the one or more likely violations of the pathway, wherein the planguides maintenance activity that reduces a likelihood of occurrence ofthe violation of the pathway by the object prior to an occurrence of theforecasted weather pattern.
 16. The computer system of claim 15, whereinthe plurality of overhead images indicate one or both of a location ofthe object and the state of the object, and wherein the plurality ofoverhead images include one or more of overhead images taken from aplurality of vantage points, overhead images taken using a variety ofspectroscopic regions, overhead images taken using a variety ofspectroscopic filters, and overhead images that have been modified by acomputer.
 17. The computer system of claim 15, wherein the forecastedweather pattern includes one or more of an average of precipitation, anaverage wind speed, an average temperature, and records ofprecipitation, wind speed, and temperature which deviate from therespective average by a specified threshold.
 18. The computer system ofclaim 15, wherein the plan for pathway management includes one or moreof a map, a prioritized list, a schedule, and a location of a predictedviolation of the pathway.
 19. The computer system of claim 18, whereinthe program instructions to analyze the three dimensional map andforecasted weather pattern to determine an object, a state of theobject, and an event that are likely to cause a violation of the pathwayarea if the forecasted weather pattern occurs includes: programinstructions to generate long-term regional-level predictions byanalyzing a plurality of weak predictors that generate short-termlocal-level predictions, wherein the long-term regional-levelpredictions indicate the object, the state of the object, and the eventthat are likely to cause the violation of the pathway area by theobject.
 20. The computer system of claim 18, wherein the analysis of theplurality of weak predictors that generates short-term local-levelpredictions includes an analysis of one or more of a local geographicfeature, the forecasted weather pattern, and a vegetation.